成为具身智能“大脑”，多模态世界模型需要具备哪些能力？丨ToB产业观察

Core Insights - The release of the Emu3.5 multimodal model by Beijing Zhiyuan Research Institute marks a significant advancement in AI technology, featuring 34 billion parameters and trained on 790 years of video data, achieving a 20-fold increase in inference speed through proprietary DiDA technology [2] - The multimodal large model market in China is projected to reach 13.85 billion yuan in 2024, growing by 67.3% year-on-year, and is expected to rise to 23.68 billion yuan in 2025 [2] - By 2025, the global multimodal large model market is anticipated to exceed 420 billion yuan, with China accounting for 35% of this market, positioning it as the second-largest single market globally [2] Multimodal Model Development - The essence of multimodal models is to enable AI to perceive the world through multiple senses, focusing on more efficient integration, deeper understanding, and broader applications [3] - A significant challenge in current multimodal technology is achieving true native unification, with about 60% of models using a "combinatorial architecture" that leads to performance degradation due to information transfer losses [3] - The Emu3.5 model utilizes a single Transformer and autoregressive architecture to achieve native unification in multimodal understanding and generation, addressing the communication issues between modalities [3] Data Challenges - Most multimodal models rely on fragmented data from the internet, such as "image-text pairs" and "short videos," which limits their ability to learn complex physical laws and causal relationships [4] - Emu3.5's breakthrough lies in its extensive use of long video data, which provides rich context and coherent narrative logic, essential for understanding how the world operates [4] - The acquisition of high-quality multimodal data is costly, and regulatory pressures regarding sensitive data in fields like healthcare and finance hinder large-scale training [4] Performance and Efficiency - Balancing performance and efficiency is a critical issue, as improvements in model performance often come at the cost of efficiency, particularly in the multimodal domain [5] - Prior to 2024, mainstream models took over 3 seconds to generate a 5-second video, with response delays in mobile applications being a significant barrier to real-time interaction [5] - The release of Emu3.5 indicates a trend where multimodal scaling laws are being validated, marking it as a potential "third paradigm" following language pre-training and post-training inference [5] Embodied Intelligence - The development of embodied intelligence is hindered by data acquisition costs and the gap between simulation and reality, which affects the performance of models in unfamiliar environments [6][7] - Emu3.5's "Next-State Prediction" capability enhances the model's understanding of physical intuition, allowing for safer and more efficient decision-making in dynamic environments [7][8] - Integrating multimodal world models into embodied intelligence could enable a unified model to process the complete cycle of perception, cognition, and action [8] Broader Applications - The impact of multimodal models extends beyond embodied intelligence, promising revolutionary applications across various sectors, including healthcare, industry, media, and transportation [9] - In healthcare, integrating multimodal capabilities with medical imaging technologies can significantly improve early disease detection and treatment precision [9][10] - The ability to generate personalized treatment plans based on extensive multimodal medical data demonstrates the transformative potential of these models in enhancing patient care and operational efficiency [10]